AIM: To investigate the regulatory effects of total ginsenosides and the conventional antihypertensive agents (captopril, amlodipine, terazosin and hydrochlorothiazide) on the blood pressure and perturbed metabolism in spontaneously hypertensive rats (SHRs) and to analyze the cause-effect relationships between high blood pressure and the metabolic disorders of hypertension.METHODS: SHRs were administrated with total ginsenosides or the antihypertensive agents for eight weeks. Systolic blood pressure (SP) was measured every week and low-molecular-weight compounds in blood plasma were quantitatively analyzed using a nontargeted high-throughput metabolomic tool: gas chromatography/time of flight mass spectrometry (GC/TOFMS) . The metabolic patterns were evaluated using principal components analysis and potential markers of hypertension were identified.RESULTS: Total ginsenosides and the antihypertensive agents differentially regulated SP and the metabolic pattern in SHRs. Total ginsenosides caused a progressive and prolonged reduction of SP and markedly normalized the perturbed metabolism with 14 of 27 (51.8%) markers of hypertension which were regulated toward normal. Total ginsenosides also reduced free fatty acids' level toward normal levels. In contrast, captopril, amlodipine and terazosin efficiently depressed SP, but had little effect on metabolic perturbation with only 8 (29.6%), 4 (14.8%), and 4 (14.8%) markers, respectively, which were regulated.CONCLUSION: The metabolic changes persisted when the blood pressure was lowered by the conventional antihypertensive agents, suggesting that hypertension may not be the cause of the metabolic perturbation in SHRs.

Essential hypertension is the most prevalent chronic disease worldwide and is a major
risk factor for cardiovascular morbidity and mortality1, 2, 3.
Although previous studies have shown that essential hypertension is a multifactorial
disease associated with metabolic disorders, its etiology and pathogenesis remain poorly
understood4, 5.
The “omics” technique offers a high-throughput screening strategy to explore
the root causes of hypertension. The genome-wide mapping of human loci has linked many
candidate genes to essential hypertension6, 7, although none of them has been widely confirmed in
different population groups. Recently, the metabolic profiling of hypertension has drawn
the attention of researchers8, 9, 10, 11,
12, 13, 14. Brindle and colleagues found a relationship between
serum metabolic profiles and blood pressure in clinic patients9. Our previous studies of clinical patients and spontaneously
hypertensive rats (SHRs) clearly showed that metabolomic patterns associated with
hypertension were different from those of the normotensive controls, demonstrating the
obviously perturbed metabolism of hypertension12,
13, 14.
Hypertension is inherently involved in metabolic syndrome, including hyperlipidemia and
diabetes. The relationship between hypertension and metabolic disorders is of great
interest. Unfortunately, although these studies have confirmed the metabolic perturbation
underlying hypertension and underscore the potential of metabonomics to investigate the
metabolic causes of hypertension, they have only investigated the hypertension and
age-related metabolic patterns of SHRs and the influence of total ginsenosides on those
patterns9, 12,
13. There has been little exploration of
metabolism in vivo or the regulatory effects of ginseng on metabolism. Detailed
metabolic variations and the regulatory effects of antihypertensive agents on metabolic
disorders have not been investigated. The study of the metabolic effects of different
types of antihypertensive agents is expected to yield comprehensive data that will provide
key insights into the metabolic perturbation of hypertension and extend our understanding
of the relationship between hypertension and metabolic syndrome.

Metabonomics represents a systematic approach to the study of metabolic phenotypes in
vivo, thereby enhancing our understanding of systems biology15. Metabonomics can be defined as the measurement of the
metabolome or the full set of low-molecular-weight endogenous compounds (or metabolites)
present within cells, tissues, organisms, or biofluids that can reflect genetic
modifications, exposure to pathogens, toxic agents, pharmaceuticals, and nutritional and
environmental changes16. Rather than assessing a
few compounds in a targeted analysis, metabonomics is a nontargeted method that analyzes
as many low-molecular-weight compounds as possible, provided the sensitivity is
sufficiently high. This set of metabolites is inherently involved in in vivo
metabolic pathways or cycles. It is not only the starting point of the metabolic system,
but also the endpoint of the metabolism that is involved in the physiological and
metabolic responses to drug interventions. In recent decades, metabonomics has shown great
utility in biomedical sciences and in the exploration of the mechanisms that underlie
disease8, 17,
18, 19, 20, 21, 22, 23.

In China, it has long been recognized that total ginsenosides (TG) can adjust blood
pressure by balancing hyper- and hypotensive states24,
25, 26. Although
dozens of investigations have reported the regulatory effects of ginseng extract or
ginseng products on lipids and sugars, these studies have offered little information on
the specific metabolites involved in these effects (with the exception of glucose and
cholesterol). The reduction of systolic blood pressure (SP) and the regulation of
metabolism in vivo mediated by TG and frontline clinical agents have never been
thoroughly investigated and correlated. To study their regulatory effects on both blood
pressure and the perturbed metabolism associated with hypertension, we used SHRs as the
animal model in this study and explored their metabolic responses to TG using a
metabonomic platform based on a gas chromatography/time-of-flight mass spectroscopy
(GC/TOFMS) technique coupled to chemometric analysis27, 28, 29. Four commercially available antihypertensive agents, which act via
diverse mechanisms, were included as positive antihypertensive control drugs.

The animal studies were carried out after approval by the Animal Ethics Committee of
China Pharmaceutical University. Male SHRs and normotensive control Wistar Kyoto rats
(WKYs), all aged 8 weeks, were purchased from Wei Tong Li Hua Animal Center (Beijing,
China). All rats were housed individually in metabolic cages in a standard animal
laboratory with a 12-h light/dark cycle. Water and standard rat chow were available
ad libitum. After 2 weeks of acclimatization, the rats were randomly divided
into groups (n=6) based on body weight to receive treatment for 8 weeks
(10–18 weeks of age). The following were administered to the rats based on the
group to which they were assigned: vehicle (0.5% carboxymethycellulose sodium aqueous
solution) or agents suspended in 1 mL of the vehicle, including total ginsenosides at 30
or 3 mg/kg (ip per day), captopril at 30 mg/kg (ig per day), amlodipine at 5 mg/kg (ig
per day), terazosin at 5 mg/kg (ig per day), or hydrochlorothiazide at 10 mg/kg (ig per
day). All of the doses were withdrawn after 8 weeks.

Measurement of SP and collection of plasma sample

The systolic blood pressures of conscious rats were measured using the indirect
plethysmographic tail cuff (Shanghai Alcott Biotech Co, Ltd, China) every week as
described previously12. After SP measurement,
0.5 mL of blood was collected in EDTA-Na anti-coagulated tubes, and the plasma was
obtained by centrifuging at 1600xg for 10 min at 4 °C and stored at
−70 °C until use.

Sample preparation and GC/TOFMS analysis

Plasma samples were thawed and the endogenous compounds were extracted, mathoximated,
and trimethylsylilated as described previously27. To minimize systematic variations, the plasma samples were
analyzed in random order. Using an Agilent 7683 autosampler, 1 μL of sample was
injected into an Agilent 6890N system equipped with a fused silica capillary column
chemically bonded with 0.18 μm DB5-MS stationary phase (10 m×0.18 mm ID). The
inlet temperature was set to 250 °C, and helium was used as the carrier gas at a
constant flow rate of 1.0 mL/min through the column. To achieve good separation, the
column temperature was initially maintained at 70 °C for 2 min and then increased at
a rate of 35 °C/min from 70 °C to 305 °C, where it was held for 2 min. The
column effluent was introduced into the ion source of a Pegasus III MS (Leco Corp, St
Joseph, MI, USA). The transferline temperature was set at 250 °C and the ion source
temperature at 200 °C. Ions were generated by a 70-eV electron beam at a current of
3.2 mA. Masses were acquired with m/z 50–680 at a rate of 30 spectra/s.

Automatic peak detection and mass spectrum deconvolution were performed as described
previously27, 28. To minimize interference from drug metabolites, only the peaks
(of the same mass spectra and retention time/index) found in the blank control
(ie, SHR control and WKY control without drug) were included in the data
matrix for further data processing. The retention index of each peak was calculated by
comparing the retention time of the peak with those of the alkane series
C8-C40. The compounds were identified by comparison of the mass
spectra and retention indices of all the detected compounds with the authentic reference
standards and those available in the National Institute of Standards and Technology
(NIST) library 2.0 (2005).

Principal component analysis and statistics

After normalization against the stable isotope internal standard, the data matrix was
constructed with the observation/samples in columns and the responses/peaks as variables
in rows. To improve the validity of the mathematical model, response variables of higher
average standard deviation (>60%) within each group were excluded from the data
matrix. Data processing was carried out using SIMCA-P 11 software (Umetrics, Umeå,
Sweden). Principal component analysis (PCA) and partial least squares projection to
latent structures & discriminant analysis (PLS-DA) were employed to process the
acquired GC/TOFMS data, following established methodology28, 29, 30. PCA involves a mathematical procedure that transforms a number of
correlated or uncorrelated variables into a smaller number of uncorrelated, new
variables called “principal components” (PCs); in other words, it projects a
K-dimensional space (K, the number of variables/peaks) and reduces it to a few PCs that
describe the maximum variation in different groups or samples while retaining as much
information as possible. Thus, the comparative analysis of data is greatly facilitated
and can be visualized; for example, in two- or three-dimensional space. PCA is an
unsupervised way to show the original scatter of the plots, whereas the supervised
methods, partial least squares projection to latent structures (PLS) and discriminant
analysis (DA), allow differentiation of the groups. The results of PCA or PLS-DA are
displayed as score plots that represent the scatter of the samples. When tightly
clustered, these indicate similar metabolic phenotypes. The relative positions of the
samples suggest their similarity or dissimilarity; when loosely clustered, they indicate
compositionally different metabolic phenotypes. The purpose of PLS-DA was to develop
models that differentiate groups or classes. Samples from the same groups were
classified into a single group for PLS_DA modeling. Cross-validation with seven
cross-validation groups was used throughout to determine the number of principal
components31.

The following statistics for the regression models are discussed throughout this paper.
R2X is the percentage of all GC/TOFMS response variables explained by the
model, R2Y is the percentage of all observation/sample variables explained by
the model, and Q2Y is the percentage of all observation/sample variables
predicted by the model. The range of these parameters is between 0 and 1, with values
approaching 1 indicating better explanation or prediction. Statistical analysis between
groups was validated for biochemical parameters or metabolite concentrations using
one-way ANOVA with a significance level of 0.05 or 0.01.

We clearly observed that the SP of the SHR control group increased gradually from week
10 to week 20, whereas the SP of the normotensive control group remained stable (Figure 1). As the model SHRs age ranged from 10 to 20 weeks, their
SP remained steady at a high level, 30%–50% higher than that of the normotensive
WKY. The SP of the animals was measured every week after treatment with TG, amlodipine,
captopril, terazosin, or hydrochlorothiazide. SP decreased rapidly and significantly in
the amlodipine-, captopril-, and terazosin-treated groups, whereas TG and
hydrochlorothiazide reduced SP only slightly (Figure 1).
Interestingly, SP was elevated during the first week of treatment in the TG group. As
the treatment continued, SP continued to decrease slowly in all groups, although the
captopril-treated group showed a sharp decline in SP after six weeks of treatment. Eight
weeks later, we stopped all treatments and SP increased quickly in all groups, with the
exception of the TG group. TG displayed its greatest antihypertensive effect on SP in
week 8, when SP was 15.6% lower than that of the SHR controls. Two weeks after the
cessation of treatment, the average increase in SP was only 8.0% of the TG group, which
was still 8.5% lower than the SP of the SHR controls (P<0.01, one-way
ANOVA). Although the SP of the captopril group reached the same level as that of the TG
group, the percentage increase was much greater, 29.6% higher than TG at week 8.
Clearly, TG yielded a persistent and sustained long-term reduction in SP, even two weeks
after the cessation of treatment. This result suggests that TG acts on an unknown target
to lower SP.

Metabolic phenotype of SHR and the regulatory effects of TG

According to the PCA algorithm, every dot represents a sample and contains information
regarding all the metabolites measured in the sample as well as their concentrations.
The metabolites and their concentrations determine the sample's position in the scores
plot. Therefore, the relative positions of the plots suggest similarities or differences
among the samples; the closer the dots, the more similar the metabolite compositions and
concentrations within the samples. Conversely, the further apart the dots, the greater
the differences among the samples. To evaluate the effects of the therapeutic agents on
the metabolic phenotype, a PLS-DA model was calculated between the SHRs and the WKY
normotensive controls between weeks 10 and 18 (ie, zero, two, four, six, and
eight weeks after treatment with the agents). This produced a good PLS-DA model of four
principal components (PC1: R2X=0.302, R2Y=0.140,
Q2Y=0.123; PC2: R2X=0.510, R2Y=0.304,
Q2Y=0.214; PC3: R2X=0.536, R2Y=0.387,
Q2Y=0.291; and PC4: R2X=0.576, R2Y=0.423,
Q2Y=0.298; Figure 2A), revealing a clear
separation of SHRs and WKYs and a continual movement in the age-related variation from
week 10 to week 18.

From week 10 to week 18, the plots moved gradually from the bottom to the top of the
figures, for both SHRs and WKYs, indicating continual metabolic modifications with
aging. However, the development of hypertension resulted in the repositioning of the SHR
plots further from the WKY plots, suggesting that the metabonomic variations became
increasingly significant.

Although the SHR data points were clearly separated from those of the normotensive
controls and moved further away with time, the profiles of the SHRs treated with TG
showed a gradual movement away from the starting point at the lower right of the
positive control and SHR plots toward the upper left, closer to that of the normotensive
WKY controls (Figure 2A). The close clustering of the TG
group and the WKY group eight weeks after the treatment strongly suggests a similarity
between the two groups in terms of their metabolic phenotypes (metabolite compositions
and concentrations). This movement pathway suggests a gradual adjustment of the
endogenous compounds in the SHRs toward those in the normotensive control. Because
endogenous metabolites are intrinsically involved in biochemical processes in
vivo, this finding suggests that TG regulates the metabolic disorders that
correlate with hypertension.

The regulatory effects on the metabolism of the antihypertensive agents were also
compared. Treatment with TG, amlodipine, captopril, terazosin, or hydrochlorothiazide
for four weeks resulted in a shift from the SHR control toward the WKY phenotype in the
TG-treated group and a slight shift in the amlodipine-treated group. However, the
effects observed in the other groups were minor (data not shown). The continuation of
the treatments for eight weeks resulted in more pronounced trends.

A snapshot of the clustering of the treated groups revealed a clear differentiation
between the treated SHRs and the SHR and WKY controls (Figure
2B). The PLS-DA model of the three principal components (R2X,
0.621; R2Y, 0.439; Q2Y, 0.286) shows that the TG group clustered
close to the normotensive control; this result suggests a metabonomic similarity. The
amlodipine-treated group was located far from both the SHR and the WKY controls,
suggesting a partial regulatory effect on the metabonomic profile. In contrast, the
groups treated with captopril, terazosin, or hydrochlorothiazide clustered close to the
SHRs control, but far from the WKY control, suggesting only a minor regulation of
metabolism. To investigate the dose-dependent metabolic response, we also examined the
effects of a low dose of TG. Our data demonstrate that in terms of the observed
metabolic adjustment, a low dose of TG was less effective than a high dose of TG.

Regulation of endogenous molecules and the metabolic network

To investigate the metabolic pathways and the specific compounds regulated by the TG
intervention, we identified compounds that exhibited differential variation in SHRs and
WKYs. In total, we identified 27 compounds (potential markers) that differed
significantly between SHRs and WKYs (Table 1). These
compounds included free fatty acids (FFAs), amino acids, lipids, and
low-molecular-weight organic acids. The variations in their levels suggested the
abnormal metabolism of lipids and the tricarboxylic acid cycle (TCA) and abnormalities
in glucose and amino acid turnover.

Treatment with TG led to the restoration of 14 (51.8%) of the 27 discriminatory
metabolites (Table 1) to normal levels, that is,
significantly different from those of the SHR control (P<0.05, one-way
ANOVA) but not different from those of the normotensive WKY control (P>0.05,
one-way ANOVA). Amlodipine, captopril, terazosin, and hydrochlorothiazide adjusted the
levels of eight (29.6%), four (14.8%), four (14.8%), and two (7.4%) metabolites toward
normal values, respectively. In contrast, an analysis of the regulatory effects of the
specific agents revealed that captopril significantly regulated α-ketoglutarate,
tryptophan, cystine, and cysteine to the normal levels observed in WKY. Amlodipine
rectified all the metabolites regulated by captopril, and citrate, pyruvate, and
creatinine. Terazosin adjusted tryptophan, cystine, cysteine, and creatinine, whereas
hydrochlorothiazide adjusted 3-hydroxybutyrate and cystine.

The regulatory effects of these antihypertensive agents were also reflected in the
diverse regulation of specific compounds. For example, high-dose TG adjusted the level
of 9-(Z)-hexadecenoic acid to normal (ie, to a similar level observed in WKYs),
which was significantly different from its level in SHRs. In contrast, the other agents
did not significantly regulate 9-(Z)-hexadecenoic acid (Supplementary Information, Figure S1A). Interestingly, TG regulated most
FFAs to normal or near normal levels. High-dose TG, amlodipine, and captopril reduced
the levels of α-ketoglutarate to normal, whereas the other drugs had a smaller
effect (Figure S1B). Only TG and hydrochlorothiamide markedly reduced the levels of
3-hydroxybutyrate, whereas the other drugs had minor effects (Figure S1C). The
regulation of arachidonic acid, fumarate, and ornithine by TG was also observed (Figure
S1D–F). In general, TG, terazosin, amlodipine, captopril, and hydrochlorothiazide
had different effects on the regulation of 9-(Z)-hexadecenoic acid, arachidonic acid,
α-ketoglutarate, 3-hydroxybutyrate, fumarate, and ornithine.

Correlation between SP and potential hypertension markers

The results discussed above show that many of the potential hypertension markers are
regulated by TG. It is important to determine whether there is a quantitative
relationship between SP and these markers. Linear regression between the SP values and
the normalized peak areas of the metabolites revealed that many lipids (eg,
oleic acid, palmitic acid, and 9-(Z)-hexadecenoic acid) correlated with SP, whereas
several amino acids (eg, serine, threonine, and ornithine) correlated inversely
with SP (Supplementary file, Figure S2). This suggests
that these compounds are not only potential markers of hypertension, but also
quantitative markers of the therapeutic response to these antihypertensive agents.

Discussion

Although metabolic dysfunction is closely related to hypertension, the cause of
hypertension remains unknown and the cause-effect relationships between hypertension and
metabolic disorders have not been established. In this study, we not only provided
evidence of the marked perturbation of lipid and glucose metabolism in SHR but also found
that TG normalizes this metabolic perturbation and shows mild, sustained antihypertensive
effects; in contrast, the conventional antihypertensive agents efficiently reduced SP but
had only minor regulatory effects on the perturbed metabolism. Captopril, amlodipine, and
terazosin act on different targets to reduce the peripheral resistance of blood vessels
and hence reduce blood pressure. This is illustrated by the following example: captopril
acts on the angiotensin-converting enzyme; amlodipine selectively inhibits calcium ion
influx across cell membranes, thus acting directly on vascular smooth muscles; terazosin
produces a hypotensive effect mainly by blocking the α1 adrenoceptors;
and hydrochlorothiazide is a diuretic agent that interferes with the renal tubular
mechanism to reduce electrolyte reabsorption. Unlike TG, these agents significantly
reduced SP, but had little regulatory effect on the perturbed metabolism of SHR, even
after the hypertension had been reduced for eight weeks. The significant regulation of the
perturbed metabolism of SHRs resulted primarily from the normalization of glucose
metabolism and lipid metabolism, whereas the effects of the other agents on the perturbed
metabolism of SHRs were probably associated with their efficient reduction of SP. The
withdrawal of the drugs led to a rapid rebound of SP. These results confirm that these
agents act on hypertension at a superficial level. Because the efficient reduction of
hypertension had only a slight effect on the perturbed metabolism, hypertension may not be
the cause of this perturbation of the metabolism.

SHRs are genetically defective in fatty acid translocase/CD3632. Studies have confirmed that defective CD36 contributes to insulin
resistance and increased serum levels of fatty acids in SHR and humans33, 34. The identified
metabolites that differentiate SHRs from WKYs (Table 1)
suggest perturbations in the metabolism of lipids, glucose, and the TCA cycle as well as
amino acid turnover (Figure S3). The elevation of FFAs in SHRs confirms the genetic defect
in the fatty acid translocase/CD36 gene. Theoretically, the downregulated expression of
the fatty acid translocase/CD36 gene will reduce the β-oxidation of FFAs and
therefore the animal's energy supply. To our surprise, we detected a marked elevation of
3-hydroxybutyrate, one of the ketone body oxidation products of FFA, possibly because SHRs
tend to utilize energy sources other than ketobodies. As supporting evidence, we observed
the elevation of TCA cycle intermediates in SHRs relative to those in WKYs. This finding
indicates that SHRs rely more on the TCA cycle for energy than on lipid metabolism. High
levels of glucose always favor the synthesis of lipids35, and we observed higher levels of FFA and glucose in SHR. However,
SHRs are smaller and thinner than WKYs. This obvious contradiction suggests that both the
catabolism and the anabolism of lipids are retarded in SHRs.

In general, the TG treatment not only led to a prolonged reduction in SP, even two weeks
after its withdrawal, but also showed a distinct regulation of the metabolic pattern
(Figure 2A, 2B) and restored the
levels of most FFAs in SHRs toward normal (Table 1). These
results suggest that TG acts on fatty acid translocase and indicates a relationship
between the SP-lowering effects of TG and its regulation of the perturbed metabolism of
SHRs. Brindle and colleagues were able to distinguish the serum samples from patients with
low/normal SP from those with borderline or high SP. However, the borderline- and high-SP
samples were indistinguishable from each other9.
This result indicates that the metabolic phenotypes of borderline and high-SP patients are
similar and that metabolic disorders have developed in borderline patients. In other
words, metabolic disorders develop before high SP can be determined, and the occurrence of
metabolic disorders does not immediately result in high SP. These lines of evidence may
explain why TG treatment clearly regulated the metabolism of SHR but did not immediately
lower SP. These results also provide indirect support for the hypothesis that TG exerts
its antihypertensive effect by normalizing the perturbed metabolism involved in
hypertension and that metabolic perturbation may play an important role in the development
of hypertension. If this hypothesis is correct, the regulation of the perturbed metabolism
will be a novel target for the treatment and cure of hypertension.

An infusion of FFAs results in elevated blood pressure in both animals and
humans36, 37,
38, 39, 40, 41, 42, 43. A large-scale population study
concluded that the elevation of FFAs in human individuals is a highly significant risk
factor for the subsequent development of hypertension44. In the present study, we not only determined the presence of
higher levels of FFAs in SHRs but also found a good correlation between SP and FFAs
(eg, oleic acid, 9-(Z)-hexadecenoic acid, and palmitic acid). Together with
evidence from other animal studies and large-scale studies in human subjects, these
results suggest that the levels of FFA or FFA-related metabolism may play an important
role in the development and remission of hypertension and that FFA may be a good biomarker
or indicator of hypertension. Based on the fact that TG significantly downregulated most
FFA toward normal levels and produced a mild and sustained reduction in SP, even two weeks
after its withdrawal, we propose that TG exerts its antihypertensive effects via the
regulation of FFA-mediated biochemical processes.

In summary, the five agents that were investigated produced different hypotensive effects
and distinct regulation patterns of the perturbed metabolism that occurs in SHRs.
Captopril, amlodipine, and terazosin efficiently reduced SP but had only a minor
regulatory effect on the perturbed metabolism of SHRs, indicating that the efficient
reduction of hypertension did not result in the correction of metabolic perturbation and
that metabolic perturbation is not the consequence of hypertension. Total ginsenosides
regulated the perturbed metabolism and showed prolonged hypotensive effects, even two
weeks after its withdrawal. These results suggest that metabolic perturbation plays an
important role in the development of hypertension and that TG is a unique antihypertensive
agent that can regulate perturbed metabolism.

This study was financially supported by the National Key New Drug Creation Special Programs
(2009ZX09304-001 and 2009ZX09502-004), the National Natural Science Foundation of China
(30630076), the Jiangsu Province Social Development Foundation (BE2008673), the Jiangsu
Nature Science Fund (BK2008038), and the National 11th 5-Year Technology Supporting Program
of China (2006BAI08B04).